This paper exploits a large number of self-labeled emotion tweets as the training data from the source domain to improve emotion identi cation in target domains (i.e., blogs and fairy tales), where there is a short supply of labeled data. Due to the noisy and ambiguous nature of self-labeled emotion training data, This paper exploits a large number of self-labeled emotion tweets as the training data from the source domain to improve emotion identi cation in target domains (i.e., blogs and fairy tales), where there is a short supply of labeled data. Due to the noisy and ambiguous nature of self-labeled emotion training data, the existing domain adaptation methods that typically depend on high-quality labeled source-domain data do not work satisfactorily. This paper describes an adaptive source-domain training instance selection method to address the problem of noisy source-domain training data. The proposed approach can e ectively identify the most informative training examples based on three carefully designed measures: consistency, diversity, and similarity. It uses an iterative method that consists of the following steps in each iteration: selecting informative samples from the source domain with the informativeness measures, merging with the target-domain training data, evaluating the performance of learned classi er for the target domain, and updating the informativeness measures for the next iteration. It stops until no new training instance is selected or in a designated number of iterations. Experiments show that our approach performs e ectively for cross-domain emotion identi cation and consistently outperforms baseline approaches across four domains.the existing domain adaptation methods that typically depend on high-quality labeled source-domain data do not work satisfactorily. This paper describes an adaptive source-domain training instance selection method to address the problem of noisy source-domain training data. The proposed approach can e ectively identify the most informative training examples based on three carefully designed measures: consistency, diversity, and similarity. It uses an iterative method that consists of the following steps in each iteration: selecting informative samples from the source domain with the informativeness measures, merging with the target-domain training data, evaluating the performance of learned classi er for the target domain, and updating the informativeness measures for the next iteration. It stops until no new training instance is selected or in a designated number of iterations. Experiments show that our approach performs e ectively for cross-domain emotion identi cation and consistently outperforms baseline approaches across four domains.